上周，我参加了麻省理工学院（MIT）的一场讲座，演讲者是尼尔·汤普森（Neil Thompson），他研究计算和人工智能的进展。仅仅半个小时，汤普森就提出了一个令人信服的论点：多个科学领域即将因人工智能而被颠覆。
Last week, I attended a talk at MIT by Neil Thompson, who studies progress in computing and artificial intelligence. Over the course of just half an hour, Thompson made a convincing case that several scientific fields are about to be upended by AI.
随着数学家们因人工智能正在自动化他们的领域而感到震惊，汤普森的最新研究表明，概率、统计理论、经济理论、高能理论、引力和宇宙学以及量子物理可能是下一个领域，这要归功于前沿模型快速发展的能力。该研究显示，在2024年中至2026年中之间，最佳人工智能模型在这些领域正确解决理论问题的能力，从大约2%的问题提升到了大约35%的问题。而且没有迹象表明这一进步会放缓。
As mathematicians reel from the way AI is automating their field, Thompson’s latest study suggests that probability, statistics theory, economic theory, high-energy theory, gravitation and cosmology, and quantum physics may be next, thanks to the rapidly evolving capabilities of frontier models. Between mid-2024 and mid-2026, the study shows, the best AI models went from being able to correctly solve about 2 percent of theoretical problems in these fields to about 35 percent of such problems. And there are no signs that this progress will slow down.
汤普森说：“在短短几年内，我们预计大量理论问题将在人工智能解决它们的能力方面被彻底改变。”
“Within just a few years, we expect a lot of theory questions to be totally transformed in terms of AI's ability to do them,” says Thompson.
总体而言，汤普森的实验室研究计算机科学的进步如何影响社会和创新。这项研究——汤普森与博士生加布里埃尔·曼索（Gabriel Manso）和研究科学家克劳迪奥·梅林克·弗尔登（Claudio Mayrink Verdun）共同进行——收集了2025年12月至2026年5月期间在线发布的研究论文中的问题，然后测试了不同的OpenAI模型——o1、o3、GPT-5、GPT-5.1、GPT-5.4和GPT-5.5——是否能够解决这些问题。
Broadly, Thompson’s lab digs into how advances in computer science impact society and affect innovation. This study—which Thompson conducted alongside Gabriel Manso, a PhD student, and Claudio Mayrink Verdun, a research scientist—took problems stated in research papers posted online between December 2025 and May 2026, and then tested whether different OpenAI models—o1, o3, GPT-5, GPT-5.1, GPT-5.4, and GPT-5.5—could solve them.
关键的是，这些问题的解答出现在GPT-5.5及之前模型训练截止时间之后发表的论文中。这意味着他们测试的模型必须自己推导出解答，而不是依赖于作为训练数据输入的答案。（这些论文作为测试模型求解能力的基准。）研究人员还阻止了互联网访问，以防止他们测试的模型在线查找解答。
Crucially, solutions to these problems were found in papers published after the training cutoff for GPT-5.5 and preceding models. This means that the models they tested had to work out solutions for themselves, rather than relying on answers fed in as training data. (Such papers served as ground truth for testing the models’ solving skills.) The researchers also blocked internet access, which prevented the models they tested from looking up solutions online.
该研究覆盖的范围有限——三人组专注于人工智能模型能够在无需运行模拟或实验的情况下自行解决问题的领域。但这项工作表明，数学领域目前所感受到的冲击波很可能向其他数学和理论密集型的科学研究领域蔓延。
The areas covered by the study were limited—the trio focused on fields in which AI models could figure things out without needing to run simulations or experiments. But the work suggests that the kinds of shock waves currently being felt in math are likely to ripple out to other math- and theory-heavy areas of scientific inquiry.
上个月，OpenAI 宣布一款未发布的模型解决了数学界长期存在的重大挑战——纳维-斯托克斯方程，这一举动震动了数学界。该方程用于解释流体运动的行为。此事在一位曾与 Anthropic 关联学者合作的纽约大学数学家指责 OpenAI 在得知其进展后抢先发布后变得更具争议性。
Last month, OpenAI rocked the math world by announcing that an unreleased model had solved a long-standing, grand challenge in mathematics known as Navier-Stokes, which concerns equations that explain the behavior of fluid motion. The episode proved extra controversial after a mathematician at NYU— who had been working with another academic associated with Anthropic—accused OpenAI of gazumping them after learning of their progress.
此后，OpenAI 及其他机构陆续宣布了数百项对长期悬而未决的数学问题的解决方案，这在该领域引发了一场类似危机。一些数学家呼吁限制人工智能的使用，警告称解决问题的过程往往和最终答案一样有价值。
OpenAI and others have since announced hundreds more solutions to longstanding math problems, triggering something of a crisis in the field. Some mathematicians have called for limits on the use of AI, warning that the process of solving problems is often as useful as having the final answer.
汤普森表示，正如数学领域一样，我们应预期其他领域的进步也将既有积极影响也有消极影响。他指出，理论上的进步有时会推动科学中的实际应用，并认为人工智能的更广泛使用可能意味着更多进展将通过这种方式实现。
As with math, Thompson says we should expect advances in other areas to have both positive and negative effects. He notes that theoretical advances sometimes drive practical advances in science and suggests that greater use of AI may mean that more progress happens this way.
一些知名人工智能研究者已经创立了专注于人工智能在科学领域应用的新实验室，而大型人工智能实验室也都在加大科学研究的投入。这些努力旨在探索人工智能如何能够在理论分析之外进行真实实验。人工智能可能意味着理论家不再需要亲自解决关键问题，但研究者表示，这并不意味着专家将不再需要——他们仍需对解决方案进行理解和验证，以确保其正确性。
Some prominent AI researchers have already founded new labs focused on AI for science, and the big AI labs are all ramping up scientific research. These efforts explore how AI could be empowered to conduct real experiments alongside theoretical analysis. AI may mean that theorists are no longer required to solve key problems, but the researchers say that doesn’t mean that experts won’t be needed to make sense of solutions and to make sure they’re sound.
“我们仍然需要人们来提出好的想法，以将这些零散的信息联系起来，”曼索说道。他补充说，对于非常复杂的问题，人工智能可能会出现错误（事实上，OpenAI在最新发布的数据集中撤回了几项自己提出的解决方案）。“我们还需要方法来验证这些解决方案的准确性，因为人工智能很容易告诉你‘这就是正确答案’，但当你仔细审视整个解决方案时，可能会发现其实并非如此。”这是《Will Knight’s AI Lab》新闻通讯的其中一期内容。
“We are still going to need people to generate good ideas to connect the dots,” says Manso. He adds that for very complex problems, it’s possible that AI might make mistakes. (OpenAI, in fact, withdrew several solutions in its latest dataset post-publication.) “We also need ways to verify what was done, because it's very easy for a model to tell you ‘this is the right answer,’ and when you look at the entire solution, you start to see maybe it is not.”This is an edition of Will Knight’s AI Lab newsletter.